The present invention relates to the domain of processing seismic images and especially to the domain of geological fault identification in these seismic images.
The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. Furthermore, all embodiments are not necessarily intended to solve all or even any of the problems brought forward in this section.
When analyzing seismic images, it may be very important do identify faults and geo-bodies to characterize the subsoil. Indeed, exploration and development teams are facing increasing challenges to find and produce hydrocarbon fields.
At the different stages of seismic interpretation, the introduction and combination of seismic attributes are keys to better understand structural and sedimentary features.
The geology derived by seismic attributes must be accurate. Uncertainties related to fault networks, geobodies mapping and their interfaces may have significant impact on prospect definition as well as on field development plans.
In order to detect faults, it is possible use classical edge detection method. It is for instance possible to receive an input seismic model and, for each mesh cell, determining the correlation value (or a mean value) with the eight closest mesh cells (in 2D).
Nevertheless, such methods have drawbacks. Indeed, edge detection method does not fully take into account the 3D information that a seismic image may have: very subtle variation may thus be ignored while they may be highly valuable for exploration and development teams.
If many methods may have been developed, they only provide coarse results and only identify obvious faults.
In addition, most of these methods detect faults based on the maximum or the minimum value of the computed results (for instance, dark lines may represent the faults if the result of the computation is represented with grey color value, 255 being the white color, 0 being the black color). Nevertheless it may be very difficult to detect subtle variations in faults as the human eye does not easily identify variation in an image that is below 10% of the value of the pixels nearby.
Thus, there is a need for detecting subtle lineaments that are not seen, or that are difficult to detect with conventional attribute maps and therefore for improving and for simplifying:                fault network and geobodies definition,        identification of flow barriers encountered,        identification of relay zones,        reducing uncertainties on fault sealing capacity and communication between panels,        field development plan, well design and geo-steering.        